文献类型：期刊文献

英文题名：Dynamic spatial cluster process model of geo-tagged tweets in London

作者：Mazzamurro, Matteo[1]; Wu, Yue[2]; Guo, Weisi[1,3]

机构：[1] Warwick Institute for Science of Cities, University of Warwick, Coventry, United Kingdom; [2] East China University of Science and Technology, School of Information Science and Technology, China; [3] Alan Turing Institute, London, United Kingdom

年份：2019

起止页码：678

外文期刊名：5th IEEE International Smart Cities Conference, ISC2 2019

收录：EI(收录号：20202008670110)

基金：This paper is partly funded by: EPSRC Centre for Doctoral Training in Urban Science and Progress (EP/L016400/1), EPSRC DTP (EP/N509796/1), and EC H2020 grant (778305), ?Corresponding Author: weisi.guo@warwick.ac.uk

语种：英文

外文关键词：Social networking (online) - Geographic information systems

摘要：Geo-tagged social media data is a key input to many smart city application areas, ranging from mapping consumer demand to understanding location dependent well-being. The sparsity in geo-tagged data, especially in certain cities, means that there is a lack of dynamic spatial point process models for social media data. Having statistically representative spatial models can enable proxy models that improve our understanding of human patterns in urban and suburban areas. Here, we analyse a data set of more than 400, 000 Tweets in London to create a spatial point process model of Tweet clusters. We model Tweet clusters as a Poisson Cluster Process. We then track how the point process parameter and spatial entropy evolve over time to create a generative model usable for others, as well as discuss its relevance to urban dynamics and smart city applications. ? 2019 IEEE.